Predicting performance by analytically solving a queueing network model

ABSTRACT

An approach is provided for predicting system performance. The approach operates by identifying a Queuing Network Model (QNM) corresponding to an information technology (IT) environment that includes a number of servers that perform a plurality of parallel services. The QNM is transformed to a linear model by serializing the parallel services as sequential services. Hardware based service demands are retrieved from the system. Software-based service demands are inferred from the hardware-based service demands. Predicted performance results of the IT environment are calculated based on the hardware-based service demands, the software-based service demands inferred from the hardware-based service demands, and the system transaction rate of the system.

BACKGROUND OF THE INVENTION

Complex IT environments contain numerous products and applications thatwork in a delicate harmony. Administration and configuration of thesesystems needs to be managed on these products as well as betweennumerous products. Using modeling to predict applications performanceunder various possible hardware and software configurations is used inCapacity Management (CM) processes. Modeling saves time and provides areduction of the cost required to buy and setup the recommended hardwarealternatives, and the test the recommended alternatives by running atest load on them. Modeling, however, faces particular challenges.Analytical approaches to QNMs are relatively fast, but unfortunately arelimited to simple QNMs that likely do not adequately represent thecomplex IT environment. Unfortunately, the complexity of multipleinteracting processes running on multiple hardware resources in acomplex IT environment makes performance prediction and capacityplanning difficult. Complex IT environments that comprise distributedsystems are inherently difficult to analyze, and this difficulty becomeseven more challenging when the environment includes “black-box”components such as software from different vendors, usually with nosource code or instrumentation available. Traditional QNMs generated bymost existing tools are complex and the model complexity grows withsystem complexity.

SUMMARY

An approach is provided for predicting system performance. The approachoperates by identifying a Queuing Network Model (QNM) corresponding toan information technology (IT) environment that includes a number ofservers that perform a plurality of parallel services. The QNM istransformed to a linear model by serializing the parallel services assequential services. Hardware based service demands are retrieved fromthe system. Software-based service demands are inferred from thehardware-based service demands. Predicted performance results of the ITenvironment are calculated based on the hardware-based service demands,the software-based service demands inferred from the hardware-basedservice demands, and the system transaction rate of the system.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present invention, asdefined solely by the claims, will become apparent in the non-limitingdetailed description set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a block diagram of a processor and components of aninformation handling system;

FIG. 2 is a network environment that includes various types ofinformation handling systems interconnected via a computer network;

FIG. 3 is a flowchart that depicts the steps and processes used topredicting performance of a complex IT environment by analyticallysolving a Queuing Network Model (QNM);

FIG. 4 is a component diagram depicting the models of the ApplicationServer threads, Data Sources, and Web Server connection of a complex ITenvironment;

FIG. 5 is a depiction of a Hardware Queuing Network (HQN) model of thecomplex IT environment;

FIG. 6A is a depiction of a Software Queuing Network (SQN) model formedby combining the three models shown in FIG. 4;

FIG. 6B is a depiction of a refined Hardware Queuing Network (HQN)formed by serializing the HQN shown in FIG. 5;

FIG. 7 is a depiction of a flowchart showing the logic performed by aprocess that performs capacity management in clustered environments;

FIG. 8A is a depiction of a multi-cluster model depicting a clusteredcomplex IT environment; and

FIG. 8B is a depiction of a serialized multi-cluster model resultingfrom serializing the model shown in FIG. 8A.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following detailed description will generally follow the summary ofthe invention, as set forth above, further explaining and expanding thedefinitions of the various aspects and embodiments of the invention asnecessary. To this end, this detailed description first sets forth acomputing environment in FIG. 1 that is suitable to implement thesoftware and/or hardware techniques associated with the invention. Anetworked environment is illustrated in FIG. 2 as an extension of thebasic computing environment, to emphasize that modern computingtechniques can be performed across multiple discrete devices.

FIG. 1 illustrates information handling system 100, which is asimplified example of a computer system capable of performing thecomputing operations described herein. Information handling system 100includes one or more processors 110 coupled to processor interface bus112. Processor interface bus 112 connects processors 110 to Northbridge115, which is also known as the Memory Controller Hub (MCH). Northbridge115 connects to system memory 120 and provides a means for processor(s)110 to access the system memory. Graphics controller 125 also connectsto Northbridge 115. In one embodiment, PCI Express bus 118 connectsNorthbridge 115 to graphics controller 125. Graphics controller 125connects to display device 130, such as a computer monitor.

Northbridge 115 and Southbridge 135 connect to each other using bus 119.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 115and Southbridge 135. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 135, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 135typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 196 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (198) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 135 to Trusted Platform Module (TPM) 195.Other components often included in Southbridge 135 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 135to nonvolatile storage device 185, such as a hard disk drive, using bus184.

ExpressCard 155 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 155 supports both PCI Expressand USB connectivity as it connects to Southbridge 135 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 135 includesUSB Controller 140 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 150, infrared(IR) receiver 148, keyboard and trackpad 144, and Bluetooth device 146,which provides for wireless personal area networks (PANs). USBController 140 also provides USB connectivity to other miscellaneous USBconnected devices 142, such as a mouse, removable nonvolatile storagedevice 145, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 145 is shown as a USB-connected device,removable nonvolatile storage device 145 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 175 connects to Southbridge 135via the PCI or PCI Express bus 172. LAN device 175 typically implementsone of the IEEE .802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 100 and another computer system or device.Optical storage device 190 connects to Southbridge 135 using Serial ATA(SATA) bus 188. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 135to other forms of storage devices, such as hard disk drives. Audiocircuitry 160, such as a sound card, connects to Southbridge 135 via bus158. Audio circuitry 160 also provides functionality such as audioline-in and optical digital audio in port 162, optical digital outputand headphone jack 164, internal speakers 166, and internal microphone168. Ethernet controller 170 connects to Southbridge 135 using a bus,such as the PCI or PCI Express bus. Ethernet controller 170 connectsinformation handling system 100 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 1 shows one information handling system, an informationhandling system may take many forms. For example, an informationhandling system may take the form of a desktop, server, portable,laptop, notebook, or other form factor computer or data processingsystem. In addition, an information handling system may take other formfactors such as a personal digital assistant (PDA), a gaming device, ATMmachine, a portable telephone device, a communication device or otherdevices that include a processor and memory.

The Trusted Platform Module (TPM 195) shown in FIG. 1 and describedherein to provide security functions is but one example of a hardwaresecurity module (HSM). Therefore, the TPM described and claimed hereinincludes any type of HSM including, but not limited to, hardwaresecurity devices that conform to the Trusted Computing Groups (TCG)standard, and entitled “Trusted Platform Module (TPM) SpecificationVersion 1.2.” The TPM is a hardware security subsystem that may beincorporated into any number of information handling systems, such asthose outlined in FIG. 2.

FIG. 2 provides an extension of the information handling systemenvironment shown in FIG. 1 to illustrate that the methods describedherein can be performed on a wide variety of information handlingsystems that operate in a networked environment. Types of informationhandling systems range from small handheld devices, such as handheldcomputer/mobile telephone 210 to large mainframe systems, such asmainframe computer 270. Examples of handheld computer 210 includepersonal digital assistants (PDAs), personal entertainment devices, suchas MP3 players, portable televisions, and compact disc players. Otherexamples of information handling systems include pen, or tablet,computer 220, laptop, or notebook, computer 230, workstation 240,personal computer system 250, and server 260. Other types of informationhandling systems that are not individually shown in FIG. 2 arerepresented by information handling system 280. As shown, the variousinformation handling systems can be networked together using computernetwork 200. Types of computer network that can be used to interconnectthe various information handling systems include Local Area Networks(LANs), Wireless Local Area Networks (WLANs), the Internet, the PublicSwitched Telephone Network (PSTN), other wireless networks, and anyother network topology that can be used to interconnect the informationhandling systems. Many of the information handling systems includenonvolatile data stores, such as hard drives and/or nonvolatile memory.Some of the information handling systems shown in FIG. 2 depictsseparate nonvolatile data stores (server 260 utilizes nonvolatile datastore 265, mainframe computer 270 utilizes nonvolatile data store 275,and information handling system 280 utilizes nonvolatile data store285). The nonvolatile data store can be a component that is external tothe various information handling systems or can be internal to one ofthe information handling systems. In addition, removable nonvolatilestorage device 145 can be shared among two or more information handlingsystems using various techniques, such as connecting the removablenonvolatile storage device 145 to a USB port or other connector of theinformation handling systems.

FIGS. 3-8B show an approach for predicting performance by analyticallysolving a Queuing Network Model (QNM) for a complex InformationTechnology (IT) environment. In this approach, modeling techniques areextended to general three-tier systems. At the same time, the approachreduces the measurement difficulties encountered when measuring the QNMparameters (service demands). In doing, the approach generates a genericmodel for three-tier systems that accounts for contention caused bysoftware resources. In particular, the number of threads of theApplication Server (AS) and the number of database Data Sources (DS) arepresented in this approach. The model generated by this approach issimple enough to be solved using time efficient analytical solvers andthe model is further extendable to handle contention caused by memorymanagement and critical sections. In this manner, the approach presentedherein improves computer systems by more accurately predicting asystem's performance and reducing the time and resources needed topredict the performance.

The approach improves the Capacity Management (CM) process by enhancingits accuracy and significantly reducing the time required to solve theresulting model. The approach therefore improves the functionality ofcomputer systems in an IT environment by utilizing a simple modelapplicable for a wide range of applications (three-tier) that can besolved relatively quickly by QNM analytical solvers, and measures thesystem service demands with single user tests available in many loadgeneration tools. The approach produces more accurate results ascompared to normal load testing results, leading to more accurate sizingof IT environments. The approach further provides separate hardware andsoftware components that allow for separate flexible sizing of thesystem and, despite providing flexible sizing, the approach is notapplication specific and does not need code level knowledge orinstrumentation. In addition, the approach is suitable for extending toother software resources, such as memory and critical sections.

The approach further provides for predicting the performance regressionof a computer system with a complex Queuing Network Model (QNM)containing clusters of servers. While traditional approaches cansimulate complex computer systems with complex QNMs for predicting theperformance counters including the Transaction Response Time (TRT) andResource Utilization (RU) for different workloads, these traditionalapproaches use simulation and are time consuming processes that can takeseveral hours for complex systems. The approach provided herein solveschallenges posed by complex QNMs in clustered environments by using atechnique that predicts the performance issues by simplifying thecomplex QNMs that contain clusters of servers and, consequently, solvingthe simplified QNM analytically, while at the same time achievingcomparable results to traditional (more time consuming) approaches. Thisapproach solves the problem of predicting performance anomalies underdifferent workloads by using a model that is solved analytically. Thisapproach is based on solving the complex QNMs for computer systems(analytically) in order to predict performance regression anomalies. Theapproach utilizes a fast technique to predict the performancecharacteristics of complex systems. The approach provides simplificationfor QNMs for predicting performance anomalies. The approach is based onmodifying the parallel servers to appear as sequential servers while, atthe same time, the approach modifies the service demands by introducingnew transaction groups—one for each extra node in the cluster, and theapproach divides the load among all of the transaction groups.

FIG. 3 is a flowchart that depicts the steps and processes used topredicting performance of a complex IT environment by analyticallysolving a Queuing Network Model (QNM). FIG. 3 processing commences at300 and shows the steps taken by a process that performs a routine topredict system performance. At step 310, the process models ApplicationServer (AS) threads as Single Queue Multiple Server (SQMS). At step 320,the process models Data Sources (DS) as SQMS node. At step 325, theprocess models Web Server (WS) Connection as SQMS node. At step 330, theprocess models all collectively in sequence (series). FIG. 4 showsexamples of the models corresponding to the various hardware modules.FIG. 4 depicts the QNM for the application server threads (model 410),the QNM for the database data sources (model 420), and the QNM for theweb server connection (model 425). FIG. 6A depicts the software QNMsmodeled collectively in sequence.

At step 335, the process applies Product Form Approximation (PFA) toHardware Queuing Network (HQN) model and Software Queuing Network (SQN)model. The result of step 335 is SQN 340 which is stored in memory area345 and HQN 355 which is stored in memory area 360. See FIG. 6A for adepiction of the SQN and FIGS. 3 and 6B for depictions of the HQN.

At step 350, the process receives input from a single user test that isused to test the hardware service demands. At step 370, the processcalculates the Hardware Service Demands (SD). The Hardware ServiceDemands include the service demands on the application server (SD(AS)),the service demands on the database server (SD(DB)), and the servicedemands on the web server (SD(WS)). At step 375, the process deduces, orinfers, the Software Service Demands (SD) based upon various hardwareservice demands that were calculated in step 370. The software servicedemand of executing the application server threads (SD(AS threads)) isapproximated as being the sum of the hardware service demands found atthe application server (SD(AS)) and the hardware service demands foundat the database server (SD(DB)). The software service demand ofproviding the database data sources (SD(DB DS)) is approximated as beingequivalent to the hardware service demands found at the database server(SD(DB)). Finally, the software service demand of executing a web serverconnection (SD(WS Conn)) is approximated as the sum of the hardwareservice demands found at the web server (SD(WS)), the hardware servicedemands found at the application server (SD(AS)) and the hardwareservice demands found at the database server (SD(DB)).

At step 380, the process inputs workload information (transaction rate)pertaining to the system that is being tested. At step 385, a CapacityManagement (CM) Process is performed using the hardware service demandscalculated in step 370, the software service demands calculated in step375, and the transaction rate received at step 380. Based on the variousinputs, at step 390, the CM process calculates a Transaction ResponseTime (TRT) for the system, a Resource Utilization (RU) for the system,and a Throughput. FIG. 3 processing thereafter ends at 395.

FIG. 4 is a component diagram depicting the models of the ApplicationServer threads, Data Sources and Web Server connection of a complex ITenvironment and FIG. 5 is a depiction of a Hardware Queuing Network(HQN) model of the complex IT environment. The Hardware Queuing Network(HQN) is a typical QNM such as the one shown in FIG. 5, where normalhardware resources such as CPU and hard disk are modeled as loadindependent queuing stations in addition to the user Think Time (TT)node which is modeled by a delay station. In FIG. 5, HQN 500 is depictedwith interaction between various hardware components in the complex ITenvironment. These hardware components include web server 510,application server 520, and database server 530.

Returning to FIG. 4, each software component (Application Serverthreads, Data Sources. and Web Server connection) is simplified andmodeled as a Single Queue Multiple Server (SQMS) environment. Model 410depicts the Application Server threads, model 420 depicts the databaseData Sources, and model 425 depicts the Web Server connection. TheSoftware Queuing Network (SQN) contains, in addition to the TT station,a set of nodes each represents a certain software module. Each softwaremodule either causes no queuing to execute in which case it isrepresented by a delay station, or it can cause queuing to execute (suchas a critical section) in which case it is represented as a loadindependent station (i.e. with a queue). For example, the criticalsection is represented by a station that has one processing unit and aqueue. A simple SQN is shown for each of the software components in FIG.4. Each software module in the SQN has a service demand on each hardwarenode in the HQN that is shown in FIG. 5.

Referring to both FIGS. 4 and 5, the total service demand on each modulein the SQN is the summation of all service demands for that module oneach station in the HQN. While, the total service demands for eachhardware station in the HQN is the summation of all service demands forthat station caused by each software module in the SQN. The SQN is firstsolved with the initial (single user) modules total service demandsusing the total number of users accessing the system. Then the number ofblocked users is calculated by finding out the number of users in eachstation queue within the SQN. The HQN is then solved with the totalnumber of users excluding the users in the queues of the SQN and thetotal service demands of the hardware stations. After solving the HQNeach module service demand (in the SQN) is set to the sum of thefraction of the residence time (queuing and serving time) of eachstation in the HQN proportional to this module contribution to theoriginal total service demands of each HQN station. Then the above isrepeated again by solving the SQN with the new service demands andsolving the HQN with the total number of users excluding the blockedusers. This continues until the number of blocked users is reasonablystable.

The application server (AS) in FIG. 5 dispatches each new job to a newthread which executes the required server module. During its executionthe thread accesses the hardware resources (CPU and hard disk) on boththe AS and the Database Server (DBS). Most of the AS code is executedwithin the thread, except the ignorable demands dispatching module, sothe SQN of the AS (520) is depicted with a single module. This modulecan be represented by a single-queue multi-server station, with a numberof servers equals to the number of the available threads in the AS.FIGS. 4 and 5 shows both the HQN and the SQN in this case. Consequently,the service demand for that single software module equals the sum ofservice demands over all the hardware resources both at the AS and theDBS. This simplification allows the service demands to be measured by asingle user test on all system resources. The SQN in FIG. 4 can besolved analytically by modeling the single-queue multi-server station inthe SQN as a load-dependent station. Solving the HQN shown in FIG. 5with the non-blocked users is solved using a analytical technique.

The HQN in FIG. 5 shows that the AS makes multiple calls to the DBS. TheDBS returns the execution to the AS after each call and finally the ASreturns back to the user (TT station). Given that the number of datasources (DS) on the DBS is limited (same as AS threads), the softwarecontention (SC) effect happens at both tiers of the system resulting ofa complex nested SQN. To address this, the approach uses anapproximation which approximates the QNM of the system (the HQN) toassume that each station is visited only once. This makes the HQN lesscomplex as the AS will make one call to the DBS which will serve the joband return back to the TT station.

FIG. 6A is a depiction of a Software Queuing Network (SQN) model formedby combining the three models shown in FIG. 4 and FIG. 6B is a depictionof a refined Hardware Queuing Network (HQN) formed by serializing theHQN shown in FIG. 5. In the same manner as explained for the HQN, and byapplying the same theoretical background, the same approximation isapplied at the SQN level shown in FIG. 4. The approach assumes the jobvisits each software module (the web server connection, the AS threads,and the DBS DS) only once. This assumption allows the modules to bedepicted sequentially with each module feeding into the next module asshown in FIG. 6A. Here, the job visits web server connection 610, thenAS threads 620, and finally DBS DS 630. The SQN contains threesingle-queue multi-server stations with each of the stations is visitedonce as shown in FIG. 6A.

Each software module in FIG. 6A only relies on the hardware servicedemands depicted in FIG. 6B within the same tier. In other words, webserver connection 610 shown in FIG. 6A relies on the CPU and hard diskhardware service demands of web server 670, application server 680, anddatabase server 690 shown in FIG. 6B. Application server 620 shown inFIG. 6A relies on the CPU and hard disk hardware service demands ofapplication server 680 and database server 690 shown in FIG. 6B.Finally, database data sources 630 shown in FIG. 6A relies on the CPUand hard disk hardware service demands of database server 690 shown inFIG. 6B.

The blocked users are calculated to include users waiting in the queuesof both single-queue multi-server stations representing the threads andDS modules. The SQN shown in FIG. 6A and the HQN shown in FIG. 6B areiteratively solved in the same manner as explained for the single tiersystem. This approximation can be generalized to n-tier systems.

FIG. 7 is a depiction of a flowchart showing the logic performed by aprocess that performs capacity management in clustered environments.FIG. 7 processing commences at 700 and shows the steps taken by aprocess that performs a capacity management routine for clusteredenvironments. At step 705, the process retrieves number the ofapplication servers and the number of resource servers used by theapplication servers from cluster configuration memory area 710. At step715, the process retrieves the number of users, the server demand data,and the application transaction group data from user memory area 720,server demand memory area 725, and transaction groups memory area 730,respectively. At step 735, the process builds a model of the clusteredsystem environment that is stored in memory area 740 (see FIG. 8A for adepiction of a clustered system environment model). At step 745, theprocess serializes all of the nodes depicted in the model stored inmemory area 740 with the serialization resulting in a serializedclustered system environment model that is stored in memory area 750(see FIG. 8B for a depiction of a serialized clustered systemenvironment model). At step 755, the process sets an instance of eachtransaction group for each of the application servers in table 760. Atstep 765, the process divides the number of users between thetransaction group instances based on a ratio that is equal to theprobability of each cluster member with the table being updated to showthe number of users for each transaction group instance. In the example,there are sixty users divided amongst six transaction group instances sothat each instance is assigned ten users. At step 770, the process setseach unused transaction group instance settings to a value of zero andsets the applicable group instance settings to actual transactionvalues. These instance settings are reflected in the updates made totable 760.

FIG. 8A is a depiction of a multi-cluster model depicting a clusteredcomplex IT environment. Multi-cluster model 740 shows a system composedof three nodes as an example this nodes are clusters of the applicationserver where the load is distributed between these servers via a loadbalancer software with a certain probability. The app server nodes (appserver 1, app server 2, through app server N) are simplified in order tosolve the complex clustered environment analytically and predict anyperformance regression anomalies. The app servers are modeled asparallel nodes. In the example, assume that the system has threeapplication servers in the cluster and that the load is 30 users whichare distributed equally among them. Also assume that the servers areidentical and the service demands n such servers are 0.05 and 0.06 sec.For each of the two classes called login and search respectively. Thisis shown in table below:

App App App DB Srvr DB Srvr # Users Serv 0 Serv 1 Serv 2 CPU Disk Trans.users (sec) (sec) (sec) (sec) (sec) (sec) Login 30 60 0.05 0.05 0.050.08 0.09 Search 30 60 0.06 0.06 0.06 0.08 0.07

FIG. 8B is a depiction of a serialized multi-cluster model resultingfrom serializing the model shown in FIG. 8A. Serialized multi-clusteredmodel 750 shows the three app servers from the example serialized alongwith the database server CPU and database server disk. Unlike theparallel app servers shown in FIG. 8A, the app servers in FIG. 8B aremodeled as serial servers. However, in order to serialize the system,new transaction groups are introduced—one for each extra node in thecluster. Each group contains the same set of transactions as what is inthe original workload. So for the example, the search and logintransactions in the original workload have additional transaction groupsadded. Here, there will be one login transaction and one searchtransaction for each app server (app server 0, 1, and 2). Login 0 andsearch 0 correspond to app server 0, login 1 and search 1 correspond toapp server 1, and Login 2 and search 2 correspond to app server 2.Consequently, the number of users is divided between the transactiongroups with a ratio equal to the probability associated with thecorresponding cluster member which, in the example, is a 0.33probability. For each unused transaction group, the service demand onthe corresponding node (application server) is set to zero. For example,search1 will have service demand of 0.06 on AppServer1 and zero onAppServer0 and AppServer2 and so on. For the other resources in thenetwork (Users, DB-Server CPU and DBServer Disk) the service demand willstay as it is in the entire transaction groups. This can be summarizedas in the below table:

DB DB App App App Srvr Srvr Users Serv 0 Serv 1 Serv 2 CPU Disk Trans. #users (sec) (sec) (sec) (sec) (sec) (sec) Login0 10 60 0.05 0.00 0.000.08 0.09 Search0 10 60 0.06 0.00 0.00 0.08 0.07 Login1 10 60 0.00 0.050.05 0.08 0.09 Search1 10 60 0.00 0.06 0.06 0.08 0.07 Login2 10 60 0.000.00 0.05 0.08 0.09 Search2 10 60 0.00 0.00 0.06 0.08 0.07

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

What is claimed is:
 1. A method, in an information handling systemcomprising one or more processors and a memory, of predicting systemperformance, the method comprising: identifying a non-linear QueuingNetwork Model (QNM) corresponding to an information technology (IT)environment, wherein the non-linear QNM includes a plurality of serversthat perform a plurality of parallel services; transforming thenon-linear QNM to a linear model by serializing the parallel services assequential services; conducting a single-user test of the ITenvironment; based on the single-user test, measuring a plurality ofhardware-based service demands related to a plurality of hardwareresources, wherein a first hardware service demand is related to a webserver, a second hardware service demand is related to an applicationserver, and a third hardware service demand is related to a databaseserver; inferring a plurality of software-based service demands on thelinear model, wherein the plurality of software-based service demandsare based on the plurality of hardware-based service demands, andwherein the inferring further comprises: estimating a first softwaredemand that is related to a demand of executing a plurality ofapplication server threads as being a sum of the second hardware servicedemand and the third hardware service demand; estimating a secondsoftware demand that is related to a demand of providing a plurality ofdatabase data sources as being equivalent to the third hardware servicedemand; and estimating a third software demand that is related to ademand of executing a web server connection by a web server as being asum of the first hardware service demand, the second hardware servicedemand, and the third hardware service demand; receiving a systemtransaction rate based on the single-user user test of the ITenvironment; and calculating one or more predicted performance resultsof the IT environment based on the plurality of hardware-based servicedemands, the plurality of software-based service demands, and the systemtransaction rate.
 2. The method of claim 1 wherein the transformingfurther comprises: generating a first model of a demand of executing aweb server connection as being executed in a first Single Queue MultipleServer (SQMS) environment; generating a second model of a demand ofexecuting a plurality of application server threads as being executed ina second SQMS environment; generating a third model of a demand ofproviding a plurality of database data sources as being executed in athird SQMS environment; and forming the linear model by modeling thefirst SQMS environment, the second SQMS environment, and the third SQMSenvironment collectively in series wherein an output from each of theSQMS environments is an input to another of the SQMS environments. 3.The method of claim 2 further comprising: applying a product formapproximation (PFA) to the first, second, and third models to combine aplurality of software resources into a single software QNM.
 4. Aninformation handling system comprising: one or more processors; a memorycoupled to at least one of the processors; a set of instructions storedin the memory and executed by at least one of the processors to predictsystem performance, wherein the set of instructions perform actions of:identifying a non-linear Queuing Network Model (QNM) corresponding to aninformation technology (IT) environment, wherein the non-linear QNMincludes a plurality of servers and that perform a plurality of parallelservices; transforming the non-linear QNM to a linear model byserializing the parallel services as sequential services; conducting asingle-user test of the IT environment; based on the single-user test,measuring a plurality of hardware-based service demands related to aplurality of hardware resources, wherein a first hardware service demandis related to a web server, a second hardware service demand is relatedto an application server, and a third hardware service demand is relatedto a database server; inferring a plurality of software-based servicedemands on the linear model, wherein the plurality of software-basedservice demands are based on the plurality of hardware-based servicedemands, wherein the inferring further comprises: estimating a firstsoftware demand that is related to a demand of executing a plurality ofapplication server threads as being a sum of the second hardware servicedemand and the third hardware service demand; estimating a secondsoftware demand that is related to a demand of providing a plurality ofdatabase data sources as being equivalent to the third hardware servicedemand; and estimating a third software demand that is related to ademand of executing a web server connection by a web server as being asum of the first hardware service demand, the second hardware servicedemand, and the third hardware service demand; receiving a systemtransaction rate based on the single-user user test of the ITenvironment; and calculating one or more predicted performance resultsof the IT environment based on the plurality of hardware-based servicedemands, the plurality of software-based service demands, and the systemtransaction rate.
 5. The information handling system of claim 4 whereinthe action of transforming further comprises: generating a first modelof a demand of executing a web server connection as being executed in afirst Single Queue Multiple Server (SQMS) environment; generating asecond model of a demand of executing a plurality of application serverthreads as being executed in a second SQMS environment; generating athird model of a demand of providing a plurality of database datasources as being executed in a third SQMS environment; and forming thelinear model by modeling the first SQMS environment, the second SQMSenvironment, and the third SQMS environment collectively in serieswherein an output from each of the SQMS environments is an input toanother of the SQMS environments.
 6. The information handling system ofclaim 5 wherein the actions further comprise: applying a product formapproximation (PFA) to the first, second, and third models to combine aplurality of software resources into a single software QNM.
 7. Acomputer program product stored in a computer readable storage medium,comprising computer instructions that, when executed by an informationhandling system, causes the information handling system to predict asystem performance by performing actions comprising: identifying anon-linear Queuing Network Model (QNM) corresponding to an informationtechnology (IT) environment, wherein the non-linear QNM includes aplurality of servers and that perform a plurality of parallel services;transforming the non-linear QNM to a linear model by serializing theparallel services as sequential services; conducting a single-user testof the IT environment; based on the single-user test, measuring aplurality of hardware-based service demands related to a plurality ofhardware resources, wherein a first hardware service demand is relatedto a web server, a second hardware service demand is related to anapplication server, and a third hardware service demand is related to adatabase server; inferring a plurality of software-based service demandson the linear model, wherein the plurality of software-based servicedemands are based on the plurality of hardware-based service demands,and wherein the inferring further comprises: estimating a first softwaredemand that is related to a demand of executing a plurality ofapplication server threads as being a sum of the second hardware servicedemand and the third hardware service demand; estimating a secondsoftware demand that is related to a demand of providing a plurality ofdatabase data sources as being equivalent to the third hardware servicedemand; and estimating a third software demand that is related to ademand of executing a web server connection by a web server as being asum of the first hardware service demand, the second hardware servicedemand, and the third hardware service demand; receiving a systemtransaction rate based on the single-user user test of the ITenvironment; and calculating one or more predicted performance resultsof the IT environment based on the plurality of hardware-based servicedemands, the plurality of software-based service demands, and the systemtransaction rate.
 8. The computer program product of claim 7 wherein theaction of transforming further comprises: generating a first model of ademand of executing a web server connection as being executed in a firstSingle Queue Multiple Server (SQMS) environment; generating a secondmodel of a demand of executing a plurality of application server threadsas being executed in a second SQMS environment; generating a third modelof a demand of providing a plurality of database data sources as beingexecuted in a third SQMS environment; forming the linear model bymodeling the first SQMS environment, the second SQMS environment, andthe third SQMS environment collectively in series wherein an output fromeach of the SQMS environments is an input to another of the SQMSenvironments; and applying a product form approximation (PFA) to thefirst, second, and third models to combine a plurality of softwareresources into a single software QNM.